Precision Diets for Diabetes Prevention

N/ACompletedNCT03919877

Stanford University115 enrolled

Overview

With this study the investigators want to understand the physiological differences for people developing pre-diabetes and diabetes. The investigators hypothesize that different individuals go through different paths in the development of the disease. By understanding the personal mechanism for developing disease, the investigators will find a personalized approach to prevent that development. The investigators are also hoping to be able to find a biomarker that will pinpoint to the particular defect and thus, diagnose the problem at an earlier stage and have the information to give personalized diet recommendations to prevent the development of diabetes more effectively.

At present, individuals with prediabetes or diabetes are grouped together as a single entity, but almost certainly they represent a mix of different gene-environment interactions that lead to one of four dominant physiologic mechanisms underlying their dysglycemia. 1- liver insulin resistance, 2- muscle insulin resistance, 3- impaired insulin secretion, 4- impaired incretin hormone secretion. Gaps that we are addressing here are extremely important - first, we will define a composite biomarker to identify different subphenotypes of prediabetes based on the four known physiologic mechanisms that contribute differentially in each individual to glucose elevations, which we hypothesize will also be reflected in their "glucotype". Importantly, because both continuous glucose monitor and administration of standardized meal testing and metabolic tests are not practical in the clinic, the development of a composite biomarker comprised of select multi-omics measures and clinical variables will enable clinicians and possibly patients (without clinician) to easily identify the specific diet that will yield optimal health results.

Study Type

INTERVENTIONAL

Allocation

Purpose

PREVENTION

Masking

NONE

Enrollment

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * Be 18 years of age or older; * Not be pregnant, if female; Exclusion Criteria: * Have major organ disease, hypertension defined as \>160/100, pregnant/lactating, diabetogenic medications, malabsorptive disorders like celiac sprue, others, heavy alcohol use, use of weight loss medications or specific diets, weight change \> 2 kg in the last three weeks, history of bariatric surgery. * Any medical condition that physicians believe would interfere with study participation or evaluation of results. * Mental incapacity a nd/or cognitive impairment on the part of the patient that would preclude adequate understanding of, or cooperation with, the study protocol.

Outcomes

Primary Outcomes

Change in Glycemic Control as Measured by Change Blood Sugar Values

Change in glycemic control measured from baseline through all phases of study, stratified according food type and metabolic sub-type. Glycemic control is derived from continuous glucose monitor (CGM) data and expressed in milligrams/deciliter.

Time frame: Assessed at a meal (2 to 6 weeks after baseline), starting just prior eating, for a period of 3 hours

Area Under the Receiver Operating Characteristic (ROC) Curve - Classification of Metabolic Subphenotype

Classify metabolic subphenotype in individuals without diabetes using a machine learning algorithm applied to the glucose time-series response generated by a 16-point (blood draws) oral glucose tolerance testing (OGTT) done in the clinical research center and at home (using CGM). Participants were categorized as insulin sensitive (IS) if teady state plasma glucose (SSPG) was \<120 mg dl-1 and insulin resistant (IR) if their SSPG was ≥120 mg dl-1. For this analysis, disposition index (DI) \< 1.58 indicates dysfunctional β-cell function, whereas DI ≥ 1.58 indicates normal β-cell function.

Time frame: Baseline (Day 1)

Secondary Outcomes

Change in Area Under the Curve (AUC) of Blood Glucose Level

Measured from baseline through all phases of study, from continuous glucose monitor (CGM) data, and stratified according food type and metabolic sub-type.